Validation and Optimal Cut-Off Score of the World Health Organization Well- being Index (WHO-5) as a Screening Tool for Depression among Patients with Schizophrenia
Bibliographic record
Abstract
Abstract Background The utility of the World Health Organization Wellbeing Index (WHO-5) as rapid screening tool for depression has not yet been researched in the context of schizophrenia. The goal of this study was twofold: (1) examine the validity and reliability of the WHO-5 in schizophrenia; (2) estimate the optimal cut-off point for the WHO-5 to screen depression in this population. Methods Chronic, remitted patients with schizophrenia took part in this study. The Calgary Depression Scale for Schizophrenia was included as index of validity. Results The results of CFA supported the originally proposed unidimensional structure of the measure, with good internal consistency reliability (α = .80), concurrent validity, and cross-sex measurement invariance. The WHO-5 showed a sensitivity of 0.81 and a specificity of 0.70 in the detection of depression with a cut-off point of 9.5. The validity of the WHO-5 as a screening tool for depression was supported by the excellent discrimination AUC value of .838. Based on this WHO-5 cut-off value, 42.6% of the patients were screened as having a depression. Conclusion The study contributes to the field by showing that the WHO-5 is a concise and convenient self-report measure for quickly screening and monitoring depressive symptoms in patients with schizophrenia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".